Simplified equalization for correlated channels in ofdma

ABSTRACT

Systems and methodologies are described that facilitate equalization of received signals in a wireless communication environment. Multiple transmit and/or receive antennas and utilize MIMO technology to enhance performance. A single tile of transmitted data, including a set of modulation symbols, can be received at multiple receive antennas, resulting in multiple tiles of received modulation symbols. Corresponding modulation symbols from multiple received tiles can be processed as a function of channel and interference estimates to generate a single equalized modulation symbol. Typically, the equalization process is computationally expensive. However, the channels are highly correlated. This correlation is reflected in the channel estimates and can be utilized to reduce complex equalization operations. In particular, a subset of the equalizers can be generated based upon the equalizer function and the remainder can be generated using interpolation. In addition, the equalizer function itself can be simplified.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 60/986,038 entitled “SIMPLIFIED EQUALIZATION FOR CORRELATEDCHANNELS IN OFDMA,” which was filed Mar. 21, 2007. The entirety of theaforementioned application is herein incorporated by reference.

BACKGROUND

I. Field

The following description relates generally to wireless communications,and, amongst other things, to facilitation of equalization.

II. Background

Wireless networking systems have become a prevalent means by which amajority of people worldwide has come to communicate. Wirelesscommunication devices have become smaller and more powerful in order tomeet consumer needs and to improve portability and convenience.Consumers have become dependent upon wireless communication devices suchas cellular telephones, personal digital assistants (PDAs) and the like,demanding reliable service, expanded areas of coverage and increasedfunctionality.

Generally, a wireless multiple-access communication system maysimultaneously support communication for multiple wireless terminals oruser devices. Each terminal communicates with one or more access pointsvia transmissions on the forward and reverse links. The forward link (ordownlink) refers to the communication link from the access points to theterminals, and the reverse link (or uplink) refers to the communicationlink from the terminals to the access points.

Wireless systems may be multiple-access systems capable of supportingcommunication with multiple users by sharing the available systemresources (e.g., bandwidth and transmit power). Examples of suchmultiple-access systems orthogonal frequency division multiple access(OFDMA) systems. Typically, each access point supports terminals locatedwithin a specific coverage area referred to as a sector. The term“sector” can refer to an access point and/or an area covered by anaccess point, depending upon context. Terminals within a sector can beallocated specific resources (e.g., time and frequency) to allowsimultaneous support of multiple terminals.

Terminals and access points can utilize multiple transmit and/or receiveantennas, referred to as multiple-input multiple output (MIMO). There issignificant interest in MIMO technology and possible increases inbandwidth in wireless systems utilizing MIMO. MIMO is designed toprovide for increases in data throughput as well as range.

SUMMARY

The following presents a simplified summary of one or more aspects inorder to provide a basic understanding of such aspects. This summary isnot an extensive overview of all contemplated aspects, and is intendedto neither identify key or critical elements of all aspects nordelineate the scope of any or all aspects. Its sole purpose is topresent some concepts of one or more aspects in a simplified form as aprelude to the more detailed description that is presented later.

In accordance with one or more aspects and corresponding disclosurethereof, various aspects are described in connection with facilitatingequalization. Access points and terminals can include multiple transmitand/or receive antennas and utilize MIMO technology to enhanceperformance. Data can be processed as a series of tiles, where a tile isa time-frequency region that includes a predetermined number ofsuccessive tones placed in a number of successive OFDM symbols. A singletile of transmitted data, including a set of modulation symbols, can bereceived by multiple receive antennas, resulting in multiple tiles ofreceived modulation symbols. Corresponding modulation symbols frommultiple received tiles can be processed as a function of channel andinterference estimates to generate a single equalized modulation symbol.Typically, the equalization process is computationally expensive. Ingeneral, a separate equalizer matrix is computed for each modulationsymbol within a tile as a function of channel and interferenceestimates. This equalizer matrix is used to generate an equalizedmodulation symbol from corresponding received modulation symbols.However, the channels are highly correlated. This correlation isreflected in the channel estimates and can be utilized to reducecomplexity of equalization operations. In particular, an equalizermatrix can be generated for each of a subset of the modulation symbolswithin a tile. The equalizer matrices for the remainder of modulationsymbols of the tile can be generated using interpolation. In otheraspects, calculation of equalizer matrices can be simplified. Forexample, an equalizer function that utilizes an inverse matrix operationcan be simplified by substituting a Taylor approximation for the inverseoperation.

In an aspect, the present disclosure provides a method for facilitatingequalization, which comprises generating an equalizer matrix for eachelement of a subset of a set of modulation symbols, wherein channelestimates associated with the set of modulation symbols are correlated.The method also comprises generating an interpolated equalizer matrixfor each element of the set of modulation symbols not included in thesubset utilizing interpolation of the equalizer matrices. In addition,the method comprises equalizing the set of modulation symbols as afunction of the equalizer matrices and the interpolated equalizermatrices.

In another aspect, the present disclosure provides an apparatus thatfacilitates equalization. The apparatus comprises a processor thatexecutes instructions for an computing equalizer matrix for a firstmodulation symbol of a set of modulation symbols, computing aninterpolated equalizer matrix for a second modulation symbol of the setof modulation symbols based at least in part upon interpolation from thefirst equalizer matrix, and computing equalized modulation symbols forthe set of modulation symbols utilizing the equalizer matrix and theinterpolated equalizer matrix. The apparatus also comprises a memorycoupled to the processor.

According to yet another aspect, the present disclosure provides anapparatus that facilitates equalization, which comprises means forgenerating equalizer matrices for a subset of a set of modulationsymbols based at least in part upon an equalizer function, whereinchannels associated with the set of modulation symbols are correlated.The apparatus also comprises means for generating matrices for the setof modulation symbols not included within the subset usinginterpolation. Additionally, apparatus comprises means for computing aset of equalized modulation symbols corresponding to the set ofmodulation symbols utilizing the equalizer matrices and the interpolatedmatrices.

According to a further aspect, the present disclosure provides acomputer-readable medium having instruction for calculating equalizermatrices for a subset of a set of modulation symbols based at least inpart upon an equalizer function, wherein channels associated with theset of modulation symbols are correlated. In addition, the mediumincludes instructions for calculating interpolated matrices for the setof modulation symbols not included within the subset based uponinterpolation of the equalizer matrices and equalizing the set ofmodulation symbols as a function of the equalizer matrices and theinterpolated matrices.

According to another aspect, the present disclosure provides a processorthat executes computer-executable instructions that facilitateequalization. The instructions comprise generating a first set ofequalizer matrices for a subset of a set of modulation symbols, whereinchannel estimates associated with the set of modulation symbols arecorrelated. The instructions also comprise generating a second set ofequalizer matrices for the set of modulation symbols not included in thesubset based at least in part upon interpolating the first set ofequalizer matrices and computing equalized modulation symbols for theset of modulation symbols based at least in part upon the first andsecond sets of equalizer matrices.

In another aspect, the present disclosure provides a method thatfacilitates equalization, which comprises generating an equalizer matrixfor each element of a subset of a set of modulation symbols based uponan equalization function. The method also comprises generating ansimplified equalizer matrix for each element of the set of modulationsymbols not included in the subset utilizing an approximation for aninverse operation of the equalization function. Additionally, the methodcomprises equalizing each element of the set of modulation symbols as afunction of the equalizer matrices and the simplified equalizermatrices.

In a further aspect, the present disclosure provides an apparatus thatfacilitates equalization, which comprises a processor that executesinstructions for computing an equalizer matrix for a first modulationsymbol based at least in part upon an equalizer function, computing asimplified equalizer matrix for a second modulation symbol based atleast in part upon a simplification of the equalizer function utilizingan approximation, and equalizing a set of modulation symbols utilizingthe equalizer matrix and the simplified equalizer matrix. The apparatusalso comprises a memory that stores equalizer information.

In yet another aspect, the present disclosure provides an apparatus thatfacilitates equalization, which comprises means for generating equalizermatrices for a subset of modulation symbols based at least in part uponan equalizer function. The apparatus also comprises means for generatingmatrices for modulation symbols using a version of the equalizerfunction, the version utilizes an approximation for an inverse operationof the equalizer function. Additionally, the apparatus comprises meansfor computing a set of equalized modulation symbols utilizing theequalizer matrices and the interpolated matrices.

In another aspect, the present disclosure provides a computer-readablemedium having instructions for calculating equalizer matrices for asubset of a set of modulation symbols based at least in part upon anequalizer function. The medium also includes instructions forcalculating approximation matrices for the set of modulation symbols notincluded within the subset based upon an approximation of an inverseoperation of the equalizer function. In addition, the medium hasinstructions for equalizing the set of modulation symbols as a functionof the equalizer matrices and the interpolated matrices.

In a further aspect, the present disclosure provides a processor thatexecutes computer-executable instructions that facilitate equalization.The instructions comprise generating a first set of equalizer matricesfor a subset of a set of modulation symbols. The instructions alsocomprise generating a second set of equalizer matrices for the set ofmodulation symbols not included in the subset utilizing a simplificationof the equalization function, the simplification utilizes anapproximation in place of an inverse matrix operation. Additionally, theinstructions comprise computing equalized modulation symbols for the setof modulation symbols based at least in part upon the first and secondsets of equalizer matrices.

To the accomplishment of the foregoing and related ends, the one or moreaspects comprise the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe annexed drawings set forth in detail certain illustrative aspects.These aspects are indicative, however, of but a few of the various waysin which the principles described herein may be employed and thedescribed aspects are intended to include their equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a wireless communication system inaccordance with one or more aspects presented herein.

FIG. 2 illustrates processing of data received from multiple receiveantennas in accordance with one or more aspects presented herein.

FIG. 3 illustrates a system that equalizes received data in accordancewith one or more aspects presented herein.

FIG. 4 is a block diagram of a system that utilizes multiple receiveantennas in accordance with one or more aspects presented herein.

FIG. 5 is block diagram of an MMSE equalizer in accordance with one ormore aspects presented herein.

FIG. 6 illustrates a methodology for equalizing received data utilizinginterpolation in accordance with one or more aspects presented herein.

FIG. 7 is block diagram of an alternate MMSE equalizer in accordancewith one or more aspects presented herein.

FIG. 8 illustrates a methodology for equalizing received data utilizingapproximations in accordance with one or more aspects presented herein.

FIG. 9 is an illustration of a wireless communication system inaccordance with one or more aspects presented herein.

FIG. 10 is an illustration of a wireless communication environment thatcan be employed in conjunction with the various systems and methodsdescribed herein.

FIG. 11 is an illustration of a system that performs simplifiedequalization utilizing interpolation in accordance with one or moreaspects presented herein.

FIG. 12 is an illustration of a system that performs simplifiedequalization utilizing approximation in accordance with one or moreaspects presented herein.

DETAILED DESCRIPTION

Various aspects are now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of one or more aspects. It may be evident, however, thatsuch aspect(s) may be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate describing one or more aspects.

As used in this application, the terms “component,” “system,” and thelike are intended to refer to an electronic device related entity,either hardware, a combination of hardware and software, software,firmware or software in execution. For example, a component may be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on acommunications device and the device can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers. Also, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate by way of local and/or remote processessuch as in accordance with a signal having one or more data packets(e.g., data from one component interacting with another component in alocal system, distributed system, and/or across a network such as theInternet with other systems by way of the signal).

Furthermore, various aspects are described herein in connection with aterminal. A terminal can also be called a system, a user device, asubscriber unit, subscriber station, mobile station, mobile device,remote station, access point, base station, remote terminal, accessterminal, user terminal, terminal, user agent, or user equipment (UE). Aterminal can be a cellular telephone, a cordless telephone, a SessionInitiation Protocol (SIP) phone, a wireless local loop (WLL) station, aPDA, a handheld device having wireless connection capability, or otherprocessing device connected to a wireless modem.

Moreover, various aspects or features described herein may beimplemented as a method, apparatus, or article of manufacture usingstandard programming and/or engineering techniques. The term “article ofmanufacture” as used herein is intended to encompass a computer programaccessible from any computer-readable device, carrier, or media. Forexample, computer readable media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical disks (e.g., compact disk (CD), digital versatile disk(DVD) . . . ), smart cards, and flash memory devices (e.g., card, stick,key drive . . . ).

Turning now to the Figures, FIG. 1 illustrates a multiple accesswireless communication system 100. Multiple access wirelesscommunication system 100 includes multiple access points 142, 144, and146. An access point provides communication coverage for a respectivegeographic area. An access point and/or its coverage area may bereferred to as a “cell”, depending on context in which the term is used.For example, the multiple access wireless communication system 100includes multiple cells 102, 104, and 106. To improve system capacity,an access point coverage area can be partitioned into multiple smallerareas, referred to as sectors. Each sector is served by a respectivebase transceiver subsystem (BTS). The term “sector” can refer to a BTSand/or its coverage area depending upon context. For a sectorized cell,the base transceiver subsystem for all sectors of the cell is typicallyco-located within the access point for the cell. The multiple sectorsmay be formed by groups of antennas each responsible for communicationwith access terminals in a portion of the cell. For example, in cell102, antenna groups 112, 114, and 116 each correspond to a differentsector. In cell 104, antenna groups 118, 120, and 122 each correspond toa different sector. In cell 106, antenna groups 124, 126, and 128 eachcorrespond to a different sector.

Each cell can include multiple access terminals that may be incommunication with one or more sectors of each access point. Forexample, access terminals 130 and 132 are in communication with accesspoint 142, access terminals 134 and 136 are in communication with accesspoint 144, and access terminals 138 and 140 are in communication withaccess point 146.

It can be seen from FIG. 1 that each access terminal 130, 132, 134, 136,138, and 140 is located in a different portion of its respective cellrelative to each other access terminal in the same cell. Further, eachaccess terminal may be at a different distance from the correspondingantenna groups with which it is communicating. Both of these factorsprovide situations, due to environmental and other conditions in thecell, which cause different channel conditions to be present betweeneach access terminal and the corresponding antenna group with which itis communicating.

For a centralized architecture, a system controller 150 is coupled toaccess points 142, 144 and 146 and provides coordination and control ofaccess points 142, 144 and 146 and further controls the routing of datafor the terminals served by these access points. For a distributedarchitecture, access points 142, 144 and 146 may communicate with oneanother as needed, e.g., to server a terminal in communication with anaccess point, to coordinate usage of subbands, and the like.Communication between access points via system controller 150 or thelike can be referred to as backhaul signaling.

As used herein, an access point can be a fixed station used forcommunicating with access terminals and may also be referred to as, andinclude some or all the functionality of, a base station. An accessterminal may also be referred to as, and include some or all thefunctionality of, a user equipment (UE), a wireless communicationdevice, a terminal, a mobile station or some other terminology.

An access point can manage multiple terminals by sharing the availablesystem resources (e.g., bandwidth and transmit power) among theterminals. For example, in an orthogonal frequency division multipleaccess (OFDMA) systems, available frequency bandwidth is divided intosegments, referred to as tiles. As used herein, a tile is a timefrequency region. Data transmissions can be processed as tiles receivedat an access terminal.

FIG. 2 illustrates an aspect that implements certain aspects of FIG. 1.In particular, FIG. 2 depicts processing of data received from multiplereceive antennas. Many access points and terminals include multipletransmit and/or receive antennas. Multiple antennas and MIMO technologycan be used to enhance data throughput and transmission range. However,reception of data at multiple receive antennas requires additional,complex processing. Typically, when a tile is transmitted a version ofthe tile is received at each antenna. The versions of the tile receivedat the different antenna are distinct due to differences in the channelsor paths between the transmit antenna and the various receive antennas.In particular, proper spacing of receive antennas ensures that thereceived tiles are distinct. The received tiles can be analyzed tocompute a single tile representative of the transmitted tile.

Generally, received data can be processed as tiles that include apredetermined number of OFDM symbols. For instance, each tile caninclude 128 modulation symbols over 16 tones. Modulation symbols caninclude data symbols as well as pilot symbols, which can be used asreferences to determine performance. Each antenna will receive atransmitted tile separately, resulting in multiple received tiles.Corresponding modulation symbols from multiple received tiles can beprocessed as a function of channel and interference estimates togenerate a single equalized modulation symbol. The number ofcomputations required to generate equalized modulation symbols increasesas the number of antennas is increased. Typically, the number ofcomputations increases linearly with the third power of the number ofreceive antennas, due to the matrix inversion operation commonly used inequalization. As the number of computations increases the time and/orprocessing power necessary to perform the computations increases.

In general, channels are highly correlated within a tile. Correlation ofchannels can be used to reduce the number of computations required toprocess data for a tile. In particular, the equalization process can besimplified, reducing the complexity of computations.

Turning once again to FIG. 2, a set of received tiles 202A, 202B, 202Cand 202D is depicted. In this particular example, a tile transmitted bya single transmit antenna (not shown) has been received at fourdifferent receive antennas (not shown), generating receive tiles 202A,202B, 202C and 202D. However, any number of transmit and/or receiveantennas can be utilized. In the illustrated example, for eachmodulation symbol in the transmitted tile, four modulation symbols arereceived, one at each antenna. The four corresponding received datasymbols 204A, 204B, 204C and 204D can be processed to generate a single,equalized modulation symbol. As illustrated, the data symbols 204A,204B, 204C and 204D are inputs for a minimum mean squared error (MMSE)equalizer 206.

The data symbols 204A, 204B, 204C and 204D are also utilized by achannel and interference estimator 208 to generate channel estimates andinterference estimates. Although a single channel and interferenceestimator 208 is illustrated for simplicity, a separate channel andinterference estimator 208 can generate channel and interferenceestimates for each receive antenna. Thus, for each receive antenna, aseparate set of channel and interference estimates can be computed. Thechannel and interference estimator 208 can compute one channel estimatefor each modulation symbol within a tile. However, the channel estimateswithin a tile, for a particular receive antenna, are highly correlated.As used herein, a channel estimate is an estimate of the response of awireless channel from a transmitter to a receiver. Channel estimation istypically performed by transmitting pilot symbols within the tiles thatare known a priori by both the transmitter and receiver. The channel andinterference estimator 208 can estimate the channel gains as a ratio ofthe received pilot symbols over the known pilot symbols. Interferencecan result from multiple transmitters transmitting their pilot signalssimultaneously. Such transmitters can be located at different accesspoints within a wireless environment, or can be different antennas ofthe same access point. Pilot interference degrades the quality of thechannel estimate. The power of the interference for the time-frequencyregion or tile is estimated and referred to herein as the interferenceestimate. MMSE 206 can utilize channel estimates, interference estimatesand received data symbols 204A, 204B, 204C and 204D to generate anequalized modulation symbol.

Referring now to FIG. 3, an aspect implementing certain aspectsdescribed with respect to FIG. 2 is illustrated. System 300 performsequalization for received data symbols. MMSE equalizer 206 can includean equalizer generator component 302 that generates an equalizationmatrix based upon channel estimates and interference estimates. Inparticular, for each modulation symbol within a transmitted tile, theequalizer generator component 302 can obtain a channel estimate and aninterference estimate corresponding to each received tile. For example,if a transmitted tile is received by four separate receive antennas,equalizer generator component 302 can obtain four separate channelestimates and four interference estimates corresponding to eachtransmitted modulation symbol.

The generated equalizer matrix can be utilized by an equalizationcomponent 304 to generate equalized modulation symbols. For example,equalization component 304 can obtain corresponding modulation symbols204A, 204B, 204C and 204D from a set of received tiles and process themodulation symbols 204A, 204B, 204C and 204D utilizing the equalizermatrix to generate a single equalized modulation symbol 306. A singlemodulation symbol 306 is generated as a function of four correspondingmodulation symbols 204A, 204B, 204C and 204D and the equalizer matrix.Here, a single modulation symbol 306 is generated because a singletransmit antenna was utilized in the example. However, if multipletransmit antennas were utilized, a separate equalized modulation symbol306 would be generated for each transmitted tile or layer.

Turning now to FIG. 4, an aspect is depicted that implements certainaspects of FIG. 2. System 400 utilizes simplified equalization togenerate equalized data symbols. A transmitter 402 can transmit a seriesof tiles. Although, a single transmitter 402 is illustrated forsimplicity, the system 400 can include multiple transmitters. Each tilecan be received by one or more receive antennas 404. While four receiveantennas 404 are illustrated, any number of antennas can be utilized.Each tile consists of a set of successive OFDM symbols transmitted overa set of contiguous tones. The number of modulation symbols within atile can be represented as N_(S)×N_(T), where N_(S) represents thenumber of OFDM symbols and N_(T) is equal to the number of tones. Asingle modulation symbol can be denoted by (t, s), where t is the toneand s is the particular OFDM symbol. Modulation symbol channels for atile can be represented as a N_(S)×N_(T) matrix H_(i,j), where iindicates the receive antenna that received the tile and j representsthe transmit antenna. Accordingly, channels for a particular modulationsymbol (t,s) can be represented by a M_(R)×R matrix H(t,s), where M_(R)is the number of receive antenna and R is the transmit antenna number.For example, if a single antenna transmits a tile received by fourantennas, modulation symbol (t,s) can be represented by a matrix H(t,s)including four separate corresponding modulation symbol channels; onefor each receive antenna 404.

Each receive antenna 404 provides data from a received tile to aseparate channel estimator 406 and an interference estimator 408. Eachchannel estimator 406 generates a channel estimate for each modulationsymbol. The channel estimates for a tile H_(i,j)(t,s) are denoted byĤ_(i,j)(t,s). For a sub-tile, the matrix of channel estimates dependonly on 3×M_(R)×R, where R is the rank (based upon the number oftransmit antennas) and M_(R) is the number of receive antennas. Choosingone modulation symbol (t₀,s₀) within the tile, channel estimates forother modulation symbols can be computed using the following exampleformula:

Ĥ _(i,j)(t,s)=Ĥ _(i,j)(t ₀ , s ₀)+(t−t ₀)Δ_(T,i,j)+(s−s ₀)Δ_(S,i,j)

Here, Δ_(T,i,j) and Δ_(S,i,j) are computed during channel estimation andrepresent the change in channel estimate between proximate tones andOFDM symbols, respectively. The matrices for channel estimates Ĥ(t,s)can be generated as follows:

Ĥ(t,s)=Ĥ(t ₀ ,s ₀)+(t−t ₀)Δ_(T)+(s−s ₀)Δ_(S)

Here, Δ_(T) and Δ_(S) are M_(R)×R matrices.

Interference estimator 408 determines an estimated interference powerfor each modulation symbol. In particular, the interference estimate foreach receive antenna i and modulation symbol (t,s) can be denoted by{circumflex over (σ)}_(i) ²(t,s). The channel and interference estimatesare computed separately for each receive antenna 404 and can be providedtogether with the received data symbols to MMSE equalizer 206.

FIG. 5, illustrates an aspect of an equalizer employing certain aspectsdescribed with respect to FIG. 3. MMSE equalizer 206 performs asimplified equalization procedure. Equalizer generator component 302 cancompute a separate equalization matrix for each modulation symbol.Typically, for each modulation symbol (t, s) an equalizer matrix can becomputed as follows:

G(t,s)=f(Ĥ(t,s),{circumflex over (σ)}₁ ²(t,s), . . . , {circumflex over(σ)}_(M) _(R) ²(t,s))

Here, G(t,s) is an R×M_(r) equalization matrix computed using equalizerfunction ƒ, based upon channel estimates and interference estimates. Avariety of functions, ƒ, can be used to generate the equalizer matrix.Once the equalization matrix has been computed, equalization component304 can compute an equalized data symbol or symbols as follows:

b(t,s)=G(t,s)x(t,s)

Here, b(t, s) is an R×1 vector of equalized modulation symbols andx(t,s) is a M_(r)×1 vector of received complex modulation symbols, whereR is the number of transmit antennas and M_(r) is the number of receiveantennas.

Computing the equalizer matrix G(t,s)=f(Ĥ(t,s),{circumflex over (σ)}₁²(t,s), . . . ,{circumflex over (σ)}_(M) _(g) ²(t,s)) for eachmodulation symbol within the tile separately can be extremely expensive.The number and/or complexity of computations can be greatly reducedbased upon channel estimates and interference estimates properties aswell as properties of the equalizer function ƒ. In particular, channelestimates for modulation symbols are correlated within the limitedboundaries of a single tile. Proximate channel estimates are notindependent and are unlikely to vary greatly among the modulationsymbols of a tile. Typically, channels vary slowly between proximatemodulation symbols. Additionally, even if channel parameters were tochange quickly, channel estimation algorithms are generally unable todetect rapid variations of a channel within a tile. Therefore,correlation of channel estimates is typically stronger than correlationamong channel parameters for a tile.

Correlation among the channel estimates is reflected within theequalizer matrix. Equalizer matrices also vary slowly with the channelestimate. Equalizers are a function of both channel estimate andinterference estimate. However, interference estimates are limited to arelatively small number of values. Typically, for a portion of the tile,a subtile, interference estimators provide only a single interferencevariance for the entire subtile. Thus interference variance estimates donot depend upon the modulation symbol (and need not be computed for eachmodulation symbol). Consequently, equalizer matrices are dependentprimarily upon the channel estimate. Based upon this correlation, therequired computations for generating equalizer matrices can be greatlyreduced without greatly impacting performance.

The equalizer generator component 302 can greatly reduce thecomputational expense by using interpolation to generate equalizermatrices. A symbol selection component 502 can identify a subset orsample of modulation symbols for which the equalizer matrices aregenerated using the equalizer function ƒ. An equalizer functioncomponent 504 can generate the equalizer matrix for the sample set asfollows:

G(t,s)=f(Ĥ(t,s),{circumflex over (σ)}₁ ²(t,s), . . . , {circumflex over(σ)}_(M) _(g) ²(t,s))

However, an interpolation component 506 can generate the remainder ofthe equalizer matrices using interpolation. In general, interpolationcan require significantly less complex computations that an equalizerfunction ƒ.

Symbol selection component 502 can utilize any suitable method foridentifying or selecting the sample set. In aspects, a predeterminedsample set can be selected. For example, the sample set can be definedsuch that the modulation symbols are evenly spread throughout the tile.

In addition, the number of modulation symbols within the sample set canvary. Using a larger sample set can increase the number of complexcomputations, however, the performance may be enhanced by an increasedsample size. The size of the sample set can be adjusted based upon theavailable processor and processing load. The size of the sample set maybe fixed based upon available resources. Alternatively, symbol selectioncomponent 502 can dynamically adapt to available resources and varysample set size to optimize performance.

Interpolation component 506 can utilize a variety of interpolationmethods to compute an equalizer matrix for those modulation symbols notincluded in the sample set. In particular, interpolation component 506can utilize linear interpolation, polynomial interpolation, and/orspline interpolation. Generally, channel estimates for a tile are highlycorrelated, as described above. Consequently, interpolation can be usedto generate a relatively accurate equalizer matrix, while requiringsignificantly fewer complex computations than typical equalizerfunctions.

Referring now to FIG. 6, an aspect implementing certain aspectsdescribed with respect to FIG. 5 is depicted. In particular, amethodology 600 that facilitates equalization of a transmitted tile isillustrated. At 602, channel and interference estimates are obtained. Inparticular, channel estimates can be generated based upon pilot symbolsincluded within a tile. Separate channel estimates and interferenceestimates are generated for each received tile. Consequently, for eachmodulation symbol, a vector of channel estimates corresponding to thereceived tiles is obtained. Similarly, a vector of interferenceestimates corresponding to received tiles is also obtained.

At 604, a subset or sample set of the modulation symbols can be selectedfor equalizer matrix computation. The number of modulation symbolsselected for the sample set can vary. In particular, the size of thesample set can be adjusted based upon available processing power.Alternatively, a predetermined subset of modulation symbols can beselected each time. For example, the sample set can contain modulationsymbols evenly distributed across the tile to facilitate interpolation.At 606, equalizer matrices can be generated for the selected modulationsymbols utilizing an equalizer function.

At 608, equalization matrices can be generated for the modulationsymbols not included within the sample set utilizing interpolation. Anyform of interpolation (e.g., linear, polynomial or spline) can beutilized to generate equalizer matrices. The equalizer matrices can beused to equalize modulation symbols received at multiple antennas at610.

FIG. 7, illustrates a further aspect of equalizer implementing certainaspects described with respect to FIG. 3. MMSE equalizer 206 utilizessimplified computation of the equalizer function. MMSE equalizer 206 canutilize a variety of equalizer functions, ƒ, to generate equalizermatrices based upon channel and interference estimates. Equalizerfunction computations can be simplified based upon correlation ofchannel estimates. In particular, equalizer functions typically includea matrix inversion computation, a computationally expensive operation.Taylor approximations can be used to approximate the matrix inversion.Simplification can be tailored to the specific equalizer function.

Typically, during MMSE equalization, for each pair (t,s), MMSE equalizerfunction, f, computes the inverse of the following matrix:

P(t,s)=(H ^(H)(t,s)H(t,s)+I)⁻¹

Here, H(t,s) denotes a matrix of channel estimates appropriately scaledby values of the interference estimates and I is the identity matrix. Tosimplify this computation, the inverse function can be replaced by afirst order Taylor approximation. To simplify the equations, H(t,s) canbe replaced by H and H(t₀,s₀) can be replaced by H₀. In addition, allchannel quantities are estimated, unless otherwise noted.

Denote P ₀=(H ₀ ^(H) H ₀ +I)⁻¹, and H=H₀+Δ.

The equation for P can be adapted as follows:

$\begin{matrix}{P = \left( {{\left( {H_{0} + \Delta} \right)^{H}\left( {H_{0} + \Delta} \right)} + I} \right)^{- 1}} \\{= \left( {{H_{0}^{H}H_{0}} + {\Delta^{H}H_{0}} + {H_{0}^{H}\Delta} + {\Delta^{H}\Delta} + I} \right)^{- 1}} \\{= \left( {\left( {{H_{0}^{H}H_{0}} + I} \right)\left( {I + {\left( {{H_{0}^{H}H_{0}} + I} \right)^{- 1}\left( {{\Delta^{H}H_{0}} + {H_{0}^{H}\Delta} + {\Delta^{H}\Delta}} \right)}} \right)} \right)^{- 1}} \\{= {\left( {I + {\left( {{H_{0}^{H}H_{0}} + I} \right)^{- 1}\left( {{\Delta^{H}H_{0}} + {H_{0}^{H}\Delta} + {\Delta^{H}\Delta}} \right)}} \right)^{- 1}\left( {{H_{0}^{H}H_{0}} + I} \right)^{- 1}}} \\{= {\left( {I + {P_{0}\left( {{\Delta^{H}H_{0}} + {H_{0}^{H}\Delta} + {\Delta^{H}\Delta}} \right)}} \right)^{- 1}P_{0}}}\end{matrix}$

Here, the Taylor approximation relies on the assumption that theeigenvalues of the following matrix are small when compared to one:

P₀(Δ^(H)H₀+H₀ ^(H)Δ+Δ^(H)Δ)

Based upon this assumption, the first order Taylor approximation of theinverse results in the following equation:

P≈(I−P ₀(Δ^(H) H ₀ +H ₀ ^(H)Δ+Δ^(H)Δ))P ₀ ≈P ₀ −P ₀(Δ^(H) H ₀ +H ₀^(H)Δ)P ₀,

Here, the term Δ^(H)Δ was dropped from the last expression, since it isassumed to be small in comparison to the other terms.

Utilizing a Taylor approximation can result in a reduction incomputational complexity. Recalling the channel estimate formula:

Ĥ(t,s)=Ĥ(t ₀ ,s ₀)+(t−t ₀)Δ_(T)+(s−s ₀)Δ_(S)

Then:

P _(t,s) ≈P ₀−(t−t ₀)P(Δ_(T) ^(H) H ₀ +H ₀ ^(H)Δ_(T))P ₀−(s−s ₀)P₀(Δ_(S) ^(H) H ₀ +H ₀ ^(H)Δ_(S))P ₀

Here, P₀ is an R×R matrix and Δ_(T), Δ_(S) and H₀ are all M_(R)×Rmatrices. Complexity can be reduced by computing the exact inverseP_(t,s) for a subset of modulation symbols (t_(i),s_(i)), referred toherein as seeds. The P_(t,s) can be computed for the remaining symbolsusing the approximation and the computed inverse for the closestmodulation symbol.

H _(t) _(i,) _(s) _(i) =H ₀+(t _(i) −t ₀)Δ_(T)+(s _(i) −s ₀)Δ_(S);

P _(t,s) ≈P _(t) _(i,) _(s) _(i) −(t−t _(i))P _(t) _(i,) _(s) _(i)(Δ_(T) ^(H) H _(t) _(i,) _(s) _(i+H) _(t) _(i,) _(s) _(i) ^(H)Δ_(T))P_(t) _(i,) _(s) _(i) −(s−s _(i))P _(t) _(i,) _(s) _(i) (Δ_(S) ^(H) H_(t) _(i,) _(s) _(i) +H _(t) _(i,) _(s) _(s) ^(H)Δ_(S))P _(t) _(i,) _(s)_(i)

Where, Δ_(T) ^(H)H_(t) _(i,) _(s) _(i) and Δ_(S) ^(H)H_(t) _(i,) _(s)_(i) can be computed as follows:

Δ_(T) ^(H) H _(t) _(i,) _(s) _(i) =Δ_(T) ^(H) H ₀+(t _(i) −t ₀)Δ_(T)^(H)Δ_(T)+(s _(i) −s ₀)Δ_(T) ^(H)Δ_(S)

Δ_(S) ^(H) H _(t) _(i,) _(s) _(i) =Δ_(S) ^(H) H ₀+(t _(i) −t ₀)Δ_(S)^(H)Δ_(T)+(s _(i) −s ₀)Δ_(S) ^(H)Δ_(S)

Turning once again to FIG. 7, equalizer generator component 302 caninclude a seed selector component 702 that determines which modulationsymbols are used as seeds, where seeds are the subset of modulationsymbols for which the inverse matrix operation will be performed. Thenumber and set of seeds can be predetermined and selected to evenlydistribute seeds within the tile. Alternatively, seed selector componentcan vary the number and/or specific modulation symbols selected as seedsbased upon performance, processing power or any other factors.

The inverse computation component 704 can perform the inverse operationand generate the equalizer matrix utilizing the original equalizerfunction for those modulation symbols selected as seeds. Anapproximation component 706 can generate equalizer matrices for theremaining modulation symbols using an approximation rather than theinverse operation. In particular, approximation component 706 canutilize a first order Taylor approximation to generate an equalizermatrices. Equalization component 304 can utilize the generated equalizermatrices to process modulation symbols and produce equalized modulationsymbols.

The use of approximations to replace inverse operations results insimplified processing. The necessary computations are evaluated below.To process a single tile utilizing approximations, the followingcomputations will be performed:

TABLE 1 Required Computations per Tile Value to be Computed ComplexMultiplications Complex Additions Δ_(T) ^(H)H₀ M_(R) × R² (M_(R) − 1) ×R² Δ_(S) ^(H)H₀ M_(R) × R² (M_(R) − 1) × R² Δ_(S) ^(H)Δ_(S) M_(R) × R²/2(M_(R) − 1) × R²/2 Δ_(T) ^(H)Δ_(S) M_(R) × R² (M_(R) − 1) × R² Δ_(T)^(H)Δ_(T) M_(R) × R²/2 (M_(R) − 1) × R²/2Each seed will require the following computations:

TABLE 2 Required Computations per Seed Complex Value to be ComputedMultiplications Additions Δ_(T) ^(H)H_(t) _(i) _(,s) _(i) 4 × R² RealMultiplications 2 × R² Δ_(S) ^(H)H_(t) _(i) _(,s) _(i) 4 × R² RealMultiplications 2 × R² P_(t) _(i) _(,s) _(i) (Δ^(H)H_(t) _(i) _(,s)_(i) + H_(t) _(i) _(,s) _(i) ^(H)Δ) R³ complex multiplications R³ P_(t)_(i) _(,s) _(i) (Δ^(H)H_(t) _(i) _(,s) _(i) + H_(t) _(i) _(,s) _(i)^(H)Δ)P_(t) _(i) _(,s) _(i) R³/2 complex multiplications R³/2 Where,Δ_(T) ^(H)H_(t) _(i) _(,s) _(i) is computed as follows: Δ_(T) ^(H)H_(t)_(i) _(,s) _(i) = Δ_(T) ^(H)H₀ + (t_(i) − t₀)Δ_(T) ^(H)Δ_(T) + (s_(i) −s₀)Δ_(T) ^(H)Δ_(S) Where Δ_(S) ^(H)H_(t) _(i) _(,s) _(i) is computed asfollows: Δ_(S) ^(H)H_(t) _(i) _(,s) _(i) = Δ_(S) ^(H)H₀ + (t_(i) −t₀)Δ_(S) ^(H)Δ_(T) + (s_(i) − s₀)Δ_(S) ^(H)Δ_(S)For each modulation symbol 2×R² additions are required.The total number of real multiplications:

Global: 16×M_(R)×R²

For each seed: 2×6×R³

Consequently, the total number of multiplications necessary for a tileis

(12×R³+8×R²+M_(R)(6R²+8R))×n_(SEEDS)+16M_(R)×R²),

Where, n_(SEEDS) is the number of seeds for which the inverse iscomputed. The reduction in total multiplications can be calculated asfollows:

$f = \frac{{M_{R}\left( {{6R^{2}} + {8R}} \right)} \times N_{S} \times N_{T}}{{\left( {{12 \times R^{3}} + {8 \times R^{2}} + {M_{R}\left( {{6R^{2}} + {8R}} \right)}} \right) \times n_{SEEDS}} + {16M_{R} \times R^{2}}}$

For example, if the number of transmit antennas and receive antennas areboth equal to four, R=M_(R)=4, the reduction in multiplications would beas follows:

$f = {\frac{512 \times N_{S} \times N_{T}}{{1408 \times n_{SEEDS}} + 1024} \approx {0.36\frac{N_{S} \times N_{T}}{n_{SEEDS} + 1}}}$

Furthermore, if the number of modulation symbols per tile is equal tosixteen and the number of tones is eight, N_(S)×N_(T)=16×8, and thenumber of seeds is equal to nine, n_(SEEDS)=9, then the number ofmultiplications is reduced by a factor of 4.65, f=4.65. The number ofadditions is equal to the following:

2×R ²×(N _(S) N _(T) −N _(SEEDS))+(M _(R)−1)×R ²×8+N _(SEEDS)×2×(4×R ²+R ³ +R/2)+2×n _(COMPLEX) _(—) _(MULT)

Referring now to FIG. 8, an aspect implementing certain aspectsdescribed with respect to FIG. 7 is depicted. In particular, amethodology 800 for simplifying equalizer function computations isillustrated. At 802, channel and interference estimates are obtained foreach of the tiles. At 804, a subset of the modulation symbols can beselected to serve as seeds. A predetermined number and set of modulationsymbols can be selected as seeds. Alternatively, seeds can be randomlyselected, selected to distribute evenly across the tile or using anyother suitable selection process. The number of seeds can be selectedbased upon performance, processing power or any other factor.

At 806, equalizer matrices can be generated for the seeds withoututilizing simplification techniques. For example, when the equalizerfunction includes an inverse operation, the inversion operation will beperformed without utilizing approximations. Equalizer matrices for theremaining modulation symbols can be generated at 808 utilizingsimplification techniques. For example, inverse operations for theremaining modulation symbols can be computed or rather approximatedusing an approximation, such as a first order Taylor approximation. At810, the equalizer can be utilized to perform equalization.

Referring to FIGS. 6 and 8, methodologies for performing equalizationare illustrated. While, for purposes of simplicity of explanation, themethodologies are shown and described as a series of acts, it is to beunderstood and appreciated that the methodologies are not limited by theorder of acts, as some acts may, in accordance with one or more aspects,occur in different orders and/or concurrently with other acts from thatshown and described herein. For example, those skilled in the art willunderstand and appreciate that a methodology could alternatively berepresented as a series of interrelated states or events, such as in astate diagram. Moreover, not all illustrated acts may be utilized toimplement a methodology in accordance with one or more aspects.

It will be appreciated that inferences can be made regardingclassification of terminal, etc. As used herein, the term to “infer” or“inference” refers generally to the process of reasoning about orinferring states of the system, environment, and/or user from a set ofobservations as captured via events and/or data. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states, for example. The inference can beprobabilistic—that is, the computation of a probability distributionover states of interest based on a consideration of data and events.Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether or not the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources.

FIG. 9 illustrates an aspect implementing certain aspects of FIG. 1.System 900 facilitates equalization in a communication environment.System 900 comprises an access point 902 with a receiver 910 thatreceives signal(s) from one or more terminals 904 through one or morereceive antennas 906, and transmits to the one or more terminals 904through a plurality of transmit antennas 908. In one or more aspects,receive antennas 906 and transmit antennas 908 can be implemented usinga single set of antennas. Receiver 910 can receive information fromreceive antennas 906 and is operatively associated with a demodulator912 that demodulates received information. Receiver 910 can be anMMSE-based receiver, or some other suitable receiver for separating outterminals assigned thereto, as will be appreciated by one skilled in theart. According to various aspects, multiple receivers can be employed(e.g., one per receive antenna), and such receivers can communicate witheach other to provide improved estimates of user data. Access point 902further comprises an equalization component 922, which can be aprocessor distinct from, or integral to, receiver 910. Equalizationcomponent 922 can utilize interpolation and/or approximations to reducethe complexity of computations required to equalize received signals.

Demodulated symbols are analyzed by a processor 914. Processor 914 iscoupled to a memory 916 that stores information related to equalization,such as the equalizer function, equalizer matrices, informationregarding the selected subset of modulation symbols for interpolation orseeds, and any other data related to equalization. It will beappreciated that the data store (e.g., memories) components describedherein can be either volatile memory or nonvolatile memory, or caninclude both volatile and nonvolatile memory. By way of illustration,and not limitation, nonvolatile memory can include read only memory(ROM), programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory caninclude random access memory (RAM), which acts as external cache memory.By way of illustration and not limitation, RAM is available in manyforms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronousDRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM(ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Thememory 916 of the subject systems and methods is intended to comprise,without being limited to, these and any other suitable types of memory.Receiver output for each antenna can be jointly processed by receiver910 and/or processor 914. A modulator 918 can multiplex the signal fortransmission by a transmitter 920 through transmit antennas 908 toterminals 904.

Referring to FIG. 10, a further aspect of FIG. 1 depicting a transmitterand receiver in a multiple access wireless communication system 1000 isillustrated. At transmitter system 1010, traffic data for a number ofdata streams is provided from a data source 1012 to a transmit (TX) dataprocessor 1014. In an embodiment, each data stream is transmitted over arespective transmit antenna. TX data processor 1014 formats, codes, andinterleaves the traffic data for each data stream based on a particularcoding scheme selected for that data stream to provide coded data. Insome embodiments, TX data processor 1014 applies precoding weights tothe symbols of the data streams based upon the user and the antenna fromwhich the symbols are being transmitted. In some embodiments, theprecoding weights may be generated based upon an index to a codebookgenerated at the transceiver, 1054 and provided as feedback to thetransceiver, 1022, which has knowledge of the codebook and its indices.Further, in those cases of scheduled transmissions, the TX dataprocessor 1014 can select the packet format based upon rank informationthat is transmitted from the user.

The coded data for each data stream may be multiplexed with pilot datausing OFDM techniques. The pilot data is typically a known data patternthat is processed in a known manner and may be used at the receiversystem to estimate the channel response. The multiplexed pilot and codeddata for each data stream is then modulated (i.e., symbol mapped) basedon a particular modulation scheme (e.g., BPSK, QSPK, M-PSK, or M-QAM)selected for that data stream to provide modulation symbols. The datarate, coding, and modulation for each data stream may be determined byinstructions performed by processor 1030. Processor 1030 can be coupledto a memory 1032 that can maintain coding scheme information. Asdiscussed above, in some embodiments, the packet format for one or morestreams may be varied according to the rank information that istransmitted from the user.

The modulation symbols for all data streams are then provided to a TXMIMO processor 1020, which may further process the modulation symbols(e.g., for OFDM). TX MIMO processor 1020 then provides N_(T) modulationsymbol streams to N_(T) transceivers (TMTR) 1022 a through 1022 t. Incertain embodiments, TX MIMO processor 1020 applies precoding weights tothe symbols of the data streams based upon the user to which the symbolsare being transmitted to and the antenna from which the symbol is beingtransmitted from that user channel response information.

Each transceiver 1022 receives and processes a respective symbol streamto provide one or more analog signals, and further conditions (e.g.,amplifies, filters, and upconverts) the analog signals to provide amodulated signal suitable for transmission over the MIMO channel. N_(T)modulated signals from transceivers 1022 a through 1022 t are thentransmitted from N_(T) antennas 1024 a through 1024 t, respectively.

At receiver system 1050, the transmitted modulated signals are receivedby NR antennas 1052 a through 1052 r and the received signal from eachantenna 1052 is provided to a respective transceiver (RCVR) 1054. Eachtransceiver 1054 conditions (e.g., filters, amplifies, and downconverts)a respective received signal, digitizes the conditioned signal toprovide samples, and further processes the samples to provide acorresponding “received” symbol stream.

An RX data processor 1060 then receives and processes the NR receivedsymbol streams from NR transceivers 1054 based on a particular receiverprocessing technique to provide N_(T) “detected” symbol streams. Theprocessing by RX data processor 1060 is described in further detailbelow. The traffic data may be provided to a data sink 1064. Processor1070 can be coupled to a memory 1072 that maintains decodinginformation. Each detected symbol stream includes symbols that areestimates of the modulation symbols transmitted for the correspondingdata stream. RX data processor 1060 then demodulates, deinterleaves, anddecodes each detected symbol stream to recover the traffic data for thedata stream. The processing by RX data processor 1060 is complementaryto that performed by TX MIMO processor 1020 and TX data processor 1014at transmitter system 1010.

The channel response estimate generated by RX processor 1060 may be usedto perform space, space/time processing at the receiver, adjust powerlevels, change modulation rates or schemes, or other actions. RXprocessor 1060 may further estimate the signal-to-noise-and-interferenceratios (SNRS) of the detected symbol streams, and possibly other channelcharacteristics, and provides these quantities to a processor 1070. RXdata processor 1060 or processor 1070 may further derive an estimate ofthe “operating” SNR for the system. Processor 1070 then providesestimated channel state information (CSI), which may comprise varioustypes of information regarding the communication link and/or thereceived data stream. For example, the CSI may comprise only theoperating SNR. The CSI is then processed by a TX data processor 1078,which also receives traffic data for a number of data streams from adata source 1076, modulated by a modulator 1080, conditioned bytransceivers 1054 a through 1054 r, and transmitted back to transmittersystem 1010.

At transmitter system 1010, the modulated signals from receiver system1050 are received by antennas 1024, conditioned by receivers 1022,demodulated by a demodulator 1040, and processed by a RX data processor1042 to recover the CSI reported by the receiver system. The demodulatedsignals can be provided to a data sink 1044. The reported quantizedinformation, e.g. CQI, is then provided to processor 1030 and used to(1) determine the data rates and coding and modulation schemes to beused for the data streams and (2) to generate various controls for TXdata processor 1014 and TX MIMO processor 1020.

The techniques described herein may be implemented by various means. Forexample, these techniques may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing units(e.g., processors 1030 and 1070, TX data processors 1014 and 1078, TXMIMO processor 1020, RX MIMO/data processor 1060, RX Data processor1042, and so on) for these techniques may be implemented within one ormore application specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, micro-controllers, microprocessors,other electronic units designed to perform the functions describedherein, or a combination thereof.

FIG. 11 depicts an aspect of certain aspects described with respect toFIG. 3. In particular, a system 1100 that facilitates equalization ofreceived signals utilizing interpolation of equalizer matrices isillustrated. Module 1102 can generate equalizer matrices for a subset ofthe modulation symbols of a tile using an equalization function. Thesubset can be selected based upon performance, processing power or anyother factor. In addition, the number and particular modulation symbolsselected for the subset can vary.

Module 1104 can generate equalizer matrices for the remaining modulationsymbols within the tile. In particular, module 1104 can utilizeinterpolation based upon the equalizer matrices generated by module 1102to generate the remaining equalizer matrices. Any method forinterpolation (e.g., linear, polynomial or spline) can be utilized.Module 1106 can generate equalized modulation symbols for the tileutilizing the equalizer matrices generated by module 1102 and module1104.

Turning now to FIG. 12, an aspect implementing certain aspects of FIG. 3is depicted. In particular, a system 1200 that facilitates equalizationutilizing simplification of the equalizer function is illustrated.Module 1202 can generate equalizer matrices for a subset of themodulation symbols of a tile using an equalization function. The subsetcan be selected based upon performance, processing power or any otherfactor. In addition, the number and particular modulation symbolsselected for the subset can vary.

Module 1204 can generate equalizer matrices for the remaining modulationsymbols utilizing a simplified version of the equalizer function. Inparticular, a first order Taylor approximation can be computed in placeof a matrix inversion. The computations required for a Taylorapproximation are typically less complex than an inverse operation,simplifying processing. Module 1206 can generate equalized modulationsymbols for the tile utilizing the equalizer matrices generated bymodule 1202 and module 1204.

For a software implementation, the techniques described herein may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. The software codes may be storedin memory units and executed by processors. The memory unit may beimplemented within the processor or external to the processor, in whichcase it can be communicatively coupled to the processor via variousmeans as is known in the art.

In one or more exemplary embodiments, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage media may be any available media that can be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that can be used to carry or store desired program code inthe form of instructions or data structures and that can be accessed bya computer. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, includes compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Combinations of the above should also beincluded within the scope of computer-readable media.

What has been described above includes examples of one or more aspects.It is, of course, not possible to describe every conceivable combinationof components or methodologies for purposes of describing theaforementioned aspects, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of variousaspects are possible. Accordingly, the described aspects are intended toembrace all such alterations, modifications and variations that fallwithin the spirit and scope of the appended claims. Furthermore, to theextent that the term “includes” is used in either the detaileddescription or the claims, such term is intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

1. A method that facilitates equalization in a receiver chain,comprising: generating an equalizer matrix for each element of a subsetof a set of modulation symbols, wherein channel estimates associatedwith the set of modulation symbols are correlated; generating aninterpolated equalizer matrix for each element of the set of modulationsymbols not included in the subset utilizing interpolation of theequalizer matrices; and equalizing the set of modulation symbols as afunction of the equalizer matrices and the interpolated equalizermatrices.
 2. The method of claim 1, further comprising selecting thesubset.
 3. The method of claim 2, the subset is selected as a functionof available processing power.
 4. The method of claim 2, the subset isdistributed evenly across the set of modulation symbols.
 5. The methodof claim 1, obtaining the channel estimates for the set of modulationsymbols, the equalizer matrix and interpolated equalizer matrix are afunction of the channel estimates.
 6. The method of claim 1, theinterpolated equalizer matrices are generated using linearinterpolation.
 7. The method of claim 1, the interpolated equalizermatrices are generated using polynomial interpolation.
 8. The method ofclaim 1, the interpolated equalizer matrices are generated using splineinterpolation.
 9. The method of claim 1, receiving the set of modulationsymbols from a plurality of transmit antennas.
 10. An apparatus thatfacilitates equalization, comprising: a processor that executesinstructions for an computing equalizer matrix for a first modulationsymbol of a set of modulation symbols, computing an interpolatedequalizer matrix for a second modulation symbol of the set of modulationsymbols based at least in part upon interpolation from the firstequalizer matrix, and computing equalized modulation symbols for the setof modulation symbols utilizing the equalizer matrix and theinterpolated equalizer matrix; and a memory coupled to the processor.11. The apparatus of claim 10, the interpolation includes at least oneof linear interpolation, polynomial interpolation, or splineinterpolation.
 12. The apparatus of claim 10, further comprisinginstructions for selecting the first modulation symbol.
 13. Theapparatus of claim 12, the first modulation symbol is randomly selected.14. The apparatus of claim 12, the first modulation symbol is a memberof a predetermined subset of the set of modulation symbols.
 15. Theapparatus of claim 10, further comprising instructions for obtainingchannel estimates and interference estimates for the set of modulationsymbols.
 16. The apparatus of claim 10, modulation symbols aretransmitted by at least two transmit antennas.
 17. An apparatus thatfacilitates equalization, comprising: means for generating equalizermatrices for a subset of a set of modulation symbols based at least inpart upon an equalizer function, wherein channels associated with theset of modulation symbols are correlated; means for generating matricesfor the set of modulation symbols not included within the subset usinginterpolation; and means for computing a set of equalized modulationsymbols corresponding to the set of modulation symbols utilizing theequalizer matrices and the interpolated matrices.
 18. The apparatus ofclaim 17, further comprising means for determining the subset.
 19. Theapparatus of claim 18, determination of the subset of is based at leastin part upon available processing power.
 20. The apparatus of claim 17,interpolation is performed using at least one of linear interpolation,polynomial interpolation or spline interpolation.
 21. The apparatus ofclaim 17, the set of modulation symbols is received from a plurality oftransmitting antennas.
 22. A computer-readable medium havinginstructions for: calculating equalizer matrices for a subset of a setof modulation symbols based at least in part upon an equalizer function,wherein channels associated with the set of modulation symbols arecorrelated; calculating interpolated matrices for the set of modulationsymbols not included within the subset based upon interpolation of theequalizer matrices; and equalizing the set of modulation symbols as afunction of the equalizer matrices and the interpolated matrices. 23.The computer-readable medium of claim 22, further comprisinginstructions for selecting the subset of the set of modulation symbols.24. The computer-readable medium of claim 22, the interpolation includesat least one of linear interpolation, polynomial interpolation, orspline interpolation.
 25. A processor that executes computer-executableinstructions that facilitate equalization, the instructions comprising:generating a first set of equalizer matrices for a subset of a set ofmodulation symbols, wherein channel estimates associated with the set ofmodulation symbols are correlated; generating a second set of equalizermatrices for the set of modulation symbols not included in the subsetbased at least in part upon interpolating the first set of equalizermatrices; and computing equalized modulation symbols for the set ofmodulation symbols based at least in part upon the first and second setsof equalizer matrices.
 26. The processor of claim 25, interpolationincludes at least one of linear interpolation, polynomial interpolation,or spline interpolation.
 27. The processor of claim 25, furthercomprising instructions for selecting the subset of the set ofmodulation symbols.
 28. The processor of claim 27, selection of thesubset is based at least in part upon available processing power. 29.The processor of claim 25, the set of modulation symbols is receivedfrom a plurality of transmit antennas.
 30. A method that facilitatesequalization in a receiver chain, comprising: generating an equalizermatrix for each element of a subset of a set of modulation symbols basedupon an equalization function; generating an simplified equalizer matrixfor each element of the set of modulation symbols not included in thesubset utilizing an approximation for an inverse operation of theequalization function; and equalizing each element of the set ofmodulation symbols as a function of the equalizer matrices and thesimplified equalizer matrices.
 31. The method of claim 30, theapproximation is a first order Taylor approximation.
 32. The method ofclaim 30, further comprising selecting the subset.
 33. The method ofclaim 32, the subset is selected based upon processing power.
 34. Anapparatus that facilitates equalization, comprising: a processor thatexecutes instructions for computing an equalizer matrix for a firstmodulation symbol based at least in part upon an equalizer function,computing a simplified equalizer matrix for a second modulation symbolbased at least in part upon a simplification of the equalizer functionutilizing an approximation, and equalizing a set of modulation symbolsutilizing the equalizer matrix and the simplified equalizer matrix; anda memory that stores equalizer information.
 35. The method of claim 34,the approximation is a first order Taylor approximation.
 36. The methodof claim 34, further comprising selecting the subset.
 37. The method ofclaim 36, the subset is selected based upon processing power.
 38. Anapparatus that facilitates equalization, comprising: means forgenerating equalizer matrices for a subset of modulation symbols basedat least in part upon an equalizer function; means for generatingmatrices for modulation symbols using a version of the equalizerfunction, the version utilizes an approximation for an inverse operationof the equalizer function; and means for computing a set of equalizedmodulation symbols utilizing the equalizer matrices and the interpolatedmatrices.
 39. The method of claim 38, the approximation is a first orderTaylor approximation.
 40. The method of claim 38, further comprisingselecting the subset.
 41. A computer-readable medium having instructionsfor: calculating equalizer matrices for a subset of a set of modulationsymbols based at least in part upon an equalizer function; calculatingapproximation matrices for the set of modulation symbols not includedwithin the subset based upon an approximation of an inverse operation ofthe equalizer function; and equalizing the set of modulation symbols asa function of the equalizer matrices and the interpolated matrices. 42.The method of claim 38, the approximation is a first order Taylorapproximation.
 43. The method of claim 38, further comprising selectingthe subset.
 44. A processor that executes computer-executableinstructions that facilitate equalization, the instructions comprising:generating a first set of equalizer matrices for a subset of a set ofmodulation symbols; generating a second set of equalizer matrices forthe set of modulation symbols not included in the subset utilizing asimplification of the equalization function, the simplification utilizesan approximation in place of an inverse matrix operation; and computingequalized modulation symbols for the set of modulation symbols based atleast in part upon the first and second sets of equalizer matrices. 45.The method of claim 44, the approximation is a first order Taylorapproximation.
 46. The method of claim 45, further comprising selectingthe subset.